Software Engineer, Search Experience

JOB DESCRIPTION

At eBay, we're more than a global ecommerce leader - we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work - every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers - and help us connect people and build communities to create economic opportunity for all.

Looking for a company that fosters passion, courage, and creativity, where you can join the team crafting the future of global commerce? Want to build how millions of people buy, sell, connect, and share around the world? If you want to be part of a purposeful community focused on developing an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

Our Search & Recommendations team provides recommendations at scale and near real-time to buyers on our website and app platforms. Recommendations play a key role in helping buyers explore eBay’s broad and diverse inventory. Our team builds powerful recommendation systems. These include live ranking, deep learning retrieval for personalized suggestions, machine-learned ranking models, real-time data pipelines, and advanced MLOps in a busy e-commerce environment.

We are developing innovative ranking and recommender systems for eBay Live and other important shopping platforms. These systems use the latest ML, NLP, LLM, and AI methods. We seek a software engineer to help compose, implement, test, and deploy reliable ranking and recommendation features at eBay scale. The candidate will collaborate with leaders and teams across our global Search & Recommendations group. This includes product managers, engineers, and applied research leaders to build, launch, monitor, and improve personalized e-commerce shopping experiences.

What you will accomplish:

Drive engineering delivery of live ranking and recommendations experiences on a variety of surfaces at eBay

Work with a team of applied researchers and engineers with deep expertise in recommender systems, machine-learned ranking, natural language processing, computer vision, large language models / AI, and ML production engineering

Develop and sustain production services, feature pipelines, model integration points, and experimentation workflows that support real-time and near-real-time recommendations

Work with large-scale distributed systems serving high-volume traffic, distributed data stores, billions of impressions per day, and low latency SLA requirements

Develop extensible and maintainable software systems for recommendation ranking, candidate enrichment, feature creation, model serving, monitoring, and deployment

Collaborate with product, analytics, applied research, and engineering team members to transform new feature requests into build options, implementation plans, production launches, and measurable business outcomes

Support iterative A/B testing by enabling experiments, analyzing results, learning from outcomes, and using those findings to drive the next feature iteration

Contribute to reusable patterns, documentation, and engineering practices that help the Live Ranking recommendations team move faster and with higher quality

What you will bring:

A Master’s degree in Computer Science or a related field with over two years of relevant experience is required. Alternatively, a Bachelor’s degree with more than four years of relevant experience in Software Engineering is acceptable.

Experience crafting and implementing efficient, practical, extensible, and maintainable software systems in an OO language such as Java, Scala, or a similar production language

Experience translating product or research requirements into technical build options, estimates, feature specs, and production-ready implementation plans

Experience working with ranking, recommendations, search, personalization, experimentation, or machine learning systems is strongly preferred

Experience working with large-scale data pipelines, streaming or batch processing, and distributed data platforms like Hadoop, Kafka, Spark, Flink, or comparable technologies is advantageous

Experience with Python, ML pipelines, feature engineering, model integration, model evaluation, or timely LLM workflows is a plus

Experience in A/B testing, experiment configuration, launch evaluation, and data-guided product iteration is a plus

Demonstrated ability to work independently on prioritized functional areas while communicating assumptions, risks, task clarifications, and tradeoffs to collaborators upfront

Proven capability to work together with product, analytics, applied research, and engineering groups to achieve quantifiable business and technical results

Previous experience publishing academic papers, patents/IP, or technical blogs is advantageous

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